Method and system for spectroscopic data analysis

ABSTRACT

A method of analyzing spectroscopic data, the method comprising collecting spatially resolved measurement spectroscopic data of a sample for a series of measurements spots, assigning the measurement spots into a predefined set of spectral categories, based on characteristics of the spectroscopic data of the respective measurement spots, identifying groupings of the measurement spots based on their respective spectral categories and their spatial relationships, and assigning each grouping of measurement spots to a fundamental sample unit data object.

This application is a Continuation application of U.S. application Ser.No. 12/368,176, filed Feb. 9, 2009, which is a Continuation applicationof U.S. application Ser. No. 10/911,057, filed on Aug. 3, 2004, which isU.S. Pat. No. 7,490,009, which are hereby incorporated by reference.

FIELD OF INVENTION

The present invention relates broadly to a method and system foranalyzing spectroscopic data, and to a computer readable medium havingstored thereon program code means for instructing a computer to executea method of analyzing spectroscopic data.

BACKGROUND

Spectroscopic tools such as electron beam induced X-ray signal or backscattered electron signal spectroscopy using a scanned electron beam canprovide data from a material sample. From that data, mineral,compositional, elemental or phase maps can be formed, or from which atspecified image points the phase, mineral composition, or elementalcomposition present at those points can be identified.

Such tools were initially mainly used for what may be referred to asfundamental research at Universities and research laboratories. Thetechnology surrounding such tools has matured to a point where they arenow more commonly found in commercial operations, such as their use bymining companies to facilitate assessment and exploration at aparticular plant or prospecting area.

As a result of this shift in the application environment for suchspectroscopic tools, a major challenge now is to provide the analyticaltools complementing the spectroscopic tools. The analytical tools shouldenable powerful and flexible processing and statistical analysis of thespectroscopic data obtained.

It is with the knowledge of the above-mentioned challenge and problemswith existing solutions that the present invention has been made, and isnow reduced to practice.

SUMMARY

In accordance with a first aspect of the present invention there isprovided a method of analyzing spectroscopic data, the methodcomprising:

collecting spatially resolved measurement spectroscopic data of a samplefor a series of measurements spots,

assigning the measurement spots into a predefined set of spectralcategories, based on characteristics of the spectroscopic data of therespective measurement spots,

identifying groupings of the measurement spots based on their respectivespectral categories and their spatial relationships, and

assigning each grouping of measurement spots to a fundamental sampleunit data object.

The method may further comprise assigning one or more properties to eachspectral category.

The method may further comprise assigning general information, includingmeasurement information and/or sample information, to each fundamentalsample unit data object.

The method may further comprise calculating one or more derivedproperties for each fundamental sample unit data object based on one ormore of a group comprising the measurement spots assigned to thefundamental sample unit data object, the properties assigned to thespectral categories of the measurement spots, the general informationassigned to the fundamental sample unit data object, and the spatialrelationships of the measurement spots.

The derived properties may comprise one or more of a group comprisingmass, area, perimeter, volume, size and density.

The predefined set of spectral categories may comprise a hierarchicalgrouping of categories.

The method may further comprise utilizing a hierarchical structure ofgeneral information data objects that embody the hierarchicalrelationships of the general information assigned to the fundamentalsample unit data objects, with relationships defined as being either“up” the hierarchy, that is away from the fundamental sample unit dataobjects, or “down” the hierarchy, that is towards the fundamental sampleunit data object, and wherein the general information assigned to afundamental sample unit data object is stored in the general informationdata object in the hierarchical structure that represents the manner inwhich the general information data is shared by the fundamental sampleunit data objects.

Data items obtainable from each general information data object in thehierarchical structure may comprise all of the data items stored in thegeneral information data object, plus all data items obtainable fromgeneral information data objects further “up” the hierarchicalstructure.

The hierarchical structure and choice of storage locations within thehierarchical structure may follow a predefined pattern.

The hierarchical structure and choice of storage locations within thehierarchical structure may be determined and changed dynamicallyas-needed.

The method may further comprise

formulating an analysis query,

defining the analysis query as a sequential series of processing stages,each processing stage having one or more inputs and one or more outputs,and

wherein, during execution of the analysis query, one or more of thefundamental sample unit data objects are sequentially provided to eachprocessing stage input as input streams, and processed and output asrespective output streams of fundamental sample unit data objects ateach processing stage output, and

wherein the output stream or streams from one processing stage are theinput streams for the next processing stage in the sequential series ofprocessing stages.

The processing at each processing stage may comprise

one or more logical expressions for assigning each fundamental sampleunit data object to one of the outputs of the processing stage, and/or

a process by which a new fundamental sample unit data object is created,differing from the original fundamental sample unit data object, butinheriting its general information and retaining a reference back to theoriginal fundamental sample unit data object.

The new fundamental sample unit data object may be created to separaterespective groupings of measurement spots which were initially assignedto one fundamental sample unit data object.

One of the processing stages may produce a statistically representativepopulation of fundamental sample unit data objects as the output streamfor normalization processing in subsequent processing stages.

The statistically representative population of fundamental sample unitdata objects may comprise fundamental sample unit data objects fromdifferent samples.

In accordance with a second aspect of the present invention there isprovided a system of analyzing spectroscopic data, the system comprising

a data collection unit for collecting spatially resolved measurementspectroscopic data of a sample for a series of measurements spots, and

a processor unit assigning the measurement spots into a predefined setof spectral categories, based on characteristics of the spectroscopicdata of the respective measurement spots, identifying groupings of themeasurement spots based on their respective spectral categories andtheir spatial relationships, and assigning each grouping of measurementspots to a fundamental sample unit data object.

The processor unit may further assign one or more properties to eachspectral category.

The processor unit may further assign general information, includingmeasurement information and/or sample information, to each fundamentalsample unit data object.

The processor unit may further calculate one or more derived propertiesfor each fundamental sample unit data object based on one or more of agroup comprising the measurement spots assigned to the fundamentalsample unit data object, the properties assigned to the spectralcategories of the measurement spots, the general information assigned tothe fundamental sample unit data object, and the spatial relationshipsof the measurement spots.

The derived properties may comprise one or more of a group comprisingmass, area, perimeter, volume, size and density.

The predefined set of spectral categories may comprise a hierarchicalgrouping of categories.

The system may further comprise a memory unit for storing a hierarchicalstructure of general information data objects that embody thehierarchical relationships of the general information assigned to thefundamental sample unit data objects, with relationships defined asbeing either “up” the hierarchy, that is away from the fundamentalsample unit data objects, or “down” the hierarchy, that is towards thefundamental sample unit data object, and wherein the general informationassigned to a fundamental sample unit data object is stored in thegeneral information data object in the hierarchical structure thatrepresents the manner in which the general information data is shared bythe fundamental sample unit data objects.

Data items obtainable from each general information data object in thehierarchical structure may comprise all of the data items stored in thegeneral information data object, plus all data items obtainable fromgeneral information data objects further “up” the hierarchicalstructure.

The hierarchical structure and choice of storage locations within thehierarchical structure may follow a predefined pattern.

The hierarchical structure and choice of storage locations within thehierarchical structure may be determined and changed dynamicallyas-needed.

The system may further comprise

an interface unit for formulating an analysis query, and

an analysis unit defining the analysis query as a sequential series ofprocessing stages, each processing stage having one or more inputs andone or more outputs, and

wherein, during execution of the analysis query, one or more of thefundamental sample unit data objects are sequentially provided to eachprocessing stage input as input streams, and processed and output asrespective output streams of fundamental sample unit data objects ateach processing stage output, and

wherein the output stream or streams from one processing stage are theinput streams for the next processing stage in the sequential series ofprocessing stages.

The processing at each processing stage may comprise

one or more logical expressions for assigning each fundamental sampleunit data object to one of the outputs of the processing stage, and/or

a process by which a new fundamental sample unit data object is created,differing from the original fundamental sample unit data object, butinheriting its general information and retaining a reference back to theoriginal fundamental sample unit data object.

The new fundamental sample unit data object may be created to separaterespective groupings of measurement spots which were initially assignedto one fundamental sample unit data object.

One of the processing stages may produce a statistically representativepopulation of fundamental sample unit data objects as the output streamfor normalization processing in subsequent processing stages.

The statistically representative population of fundamental sample unitdata objects may comprise fundamental sample unit data objects fromdifferent samples.

In accordance with a third aspect of the present invention there isprovided a computer readable data storage medium having stored thereonprogram code means for instructing a computer to execute a method ofanalyzing spectroscopic data, the method comprising

collecting spatially resolved measurement spectroscopic data of a samplefor a series of measurements spots,

assigning the measurement spots into a predefined set of spectralcategories, based on characteristics of the spectroscopic data of therespective measurement spots,

identifying groupings of the measurement spots based on their respectivespectral categories and their spatial relationships, and

assigning each grouping of measurement spots to a fundamental sampleunit data object.

In accordance with a fourth aspect of the present invention there isprovided a method of analyzing spectroscopic data, the method comprising

collecting spatially resolved measurement spectroscopic data of a samplefor a series of measurements spots,

assigning the measurement spots into a predefined set of spectralcategories, based on characteristics of the spectroscopic data of therespective measurement spots,

identifying groupings of the measurement spots based on their respectivespectral categories and their spatial relationships,

assigning each grouping of measurement spots to a fundamental sampleunit data object;

assigning general information, including measurement information and/orsample information, to each fundamental sample unit data object;

wherein, during sequential processing of the fundamental sample unitdata objects in a processing stage, one or more new fundamental sampleunit data objects based on an original fundamental sample unit dataobject are created, wherein the new fundamental sample unit data objectsinherit the general information assigned to the original fundamentalsample unit data object and retain a reference back to the originalfundamental sample unit data object, and each new fundamental sampleunit data object is passed to one or more processing stage outputs.

In accordance with a fifth aspect of the present invention there isprovided a system analyzing spectroscopic data, the system comprising

a data collection unit for collecting spatially resolved measurementspectroscopic data of a sample for a series of measurements spots, and

a processor unit assigning the measurement spots into a predefined setof spectral categories, based on characteristics of the spectroscopicdata of the respective measurement spots, identifying groupings of themeasurement spots based on their respective spectral categories andtheir spatial relationships, assigning each grouping of measurementspots to a fundamental sample unit data object, assigning generalinformation, including measurement information and/or sampleinformation, to each fundamental sample unit data object;

wherein, during sequential processing of the fundamental sample unitdata objects in a processing stage, the processor unit creates one ormore new fundamental sample unit data objects based on an originalfundamental sample unit data object, wherein the new fundamental sampleunit data objects inherit the general information assigned to theoriginal fundamental sample unit data object and retain a reference backto the original fundamental sample unit data object, and passes each newfundamental sample unit data object to one or more processing stageoutputs.

In accordance with a sixth aspect of the present invention there isprovided a computer readable data storage medium having stored thereonprogram code means for instructing a computer to execute a method ofanalyzing spectroscopic data, the method comprising

collecting spatially resolved measurement spectroscopic data of a samplefor a series of measurements spots,

assigning the measurement spots into a predefined set of spectralcategories, based on characteristics of the spectroscopic data of therespective measurement spots,

identifying groupings of the measurement spots based on their respectivespectral categories and their spatial relationships,

assigning each grouping of measurement spots to a fundamental sampleunit data object;

assigning general information, including measurement information and/orsample information, to each fundamental sample unit data object;

wherein, during sequential processing of the fundamental sample unitdata objects in a processing stage, one or more new fundamental sampleunit data objects based on an original fundamental sample unit dataobject are created, wherein the new fundamental sample unit data objectsinherit the general information assigned to the original fundamentalsample unit data object and retain a reference back to the originalfundamental sample unit data object, and each new fundamental sampleunit data object is passed to one or more processing stage outputs.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be better understood and readilyapparent to one of ordinary skill in the art from the following writtendescription, by way of example only, and in conjunction with thedrawings, in which:

FIG. 1 is a schematic drawing illustrating a spectroscopic tool forconducting sample measurements.

FIG. 2 is a schematic drawing illustrating the relationship betweenmeasurement spots and fundamental sample units according to anembodiment of the present invention.

FIG. 3 is a schematic flow chart illustrating a Job Particle Collectionprocess according to an embodiment of the present invention.

FIG. 4 is a schematic flow chart illustrating a Report process accordingto an embodiment of the present invention.

FIG. 5 shows a screen shot of a preprocessor in the process of FIG. 4.

FIG. 6 shows a screen shot of a Report according to an embodiment of thepresent invention.

FIG. 7 shows a screen shot of another Report according to an embodimentof the present invention.

FIG. 8 shows a schematic drawing of a computer system for implementing aspectroscopic data analysis system according to an embodiment of thepresent invention.

DETAILED DESCRIPTION

FIG. 1 is a schematic drawing illustrating the components of aspectroscopic system for quantitative evaluation of materials byscanning electron microscopy. The system comprises a Scanning ElectronMicroscope (SEM) component 10 consisting of an electron beam source 12producing a collimated beam 14 of electrons, which is directed onto asample 16 to be analyzed. The SEM 10 further comprises a deflectingmeans 18 to cause the beam 14 to scan the surface of the sample 16 in aspatially resolved manner, for example a suitable raster pattern such asthe series of parallel lines 20 shown in FIG. 1.

The sample is typically part of a sample block and each sample block maybe part of a series of sample blocks. The SEM may include a mechanicalstage system for selecting a series of “fields of view” on a givensample block and/or a series of sample blocks for measurement.

In operation, the beam 14 is moved along each line in succession and iscaused to pause at successive ones of a series of spots e.g. 22 in eachline. X-ray photons and back scattered electrons produced at each of thespots e.g. 22 pass to two detectors 24, 26 respectively, for collectingof X-ray signal spectroscopic data and BSE signal spectroscopic dataassociated with the respective spots e.g. 22. The X-ray detector 24 inthe example embodiment is an energy dispersive detector and produces,for each spot e.g. 22, a time spaced series or spectrum of X-ray signalsof amplitudes which are representative of energies of X-rays generatedat that spot e.g. 22 pursuant to the incidence of the beam 14 thereon.

BSE detector 26 in the example embodiment similarly produces an analoguesignal representative of the intensity of back scattered electrons atthe spot e.g. 22 upon which the beam 14 is incident.

For a detailed description of a possible operation scheme or techniquefor systems for quantitative evaluation of materials with systems suchas the one described above with reference to FIG. 1, reference is madeto U.S. Pat. No. 4,476,386, the contents of which are herebyincorporated by cross reference.

Based on these general principles relating to the obtaining ofspectroscopic data from a physical sample, an example embodiment of thepresent invention will now be described.

Depending on, inter alia, the size of the probe beam, the rasterparameters, and the actual sample, spectral data measurements are madeand grouped into clusters that correspond to the physical particles inthe sample e.g. 202, 204, 206 as illustrated in FIG. 2. Each measuredspot e.g. 208 is assigned a spectral category, by comparing the measuredspectral data from that spot to a predefined set of spectral categories.A particle entity is created for each cluster e.g. 210 of measured spotse.g. 208 identified as belonging to one physical particle e.g. 202 inthe example embodiment.

Each particle entity e.g. 202, 204, 206 is uniquely identified withinthe data structure of the example embodiment. The data representation ofeach particle entity 202, 204, 206 functions as a fundamental sampleunit. All further data entities in the data structure of the exampleembodiment exist for the purposes of organizing, processing or analyzingthe fundamental sample unit entities.

The particle entities retain their unique identity throughout theanalysis, which means that, at any time, the identity and uniqueproperties of any particle entity can be determined. By dealing withparticle entities as the fundamental unit in the example embodiment, andretaining the unique identities, it is possible to perform a variety ofad-hoc statistical analyses on a population of measured particleentities. It also makes it possible to easily identify and extractsub-populations from the particle entities.

As a key feature of the example embodiment, while basic categories (forexample physical properties such as density or chemical composition) areassigned at the measurement spot level, according to spectral category,derived properties are calculated at the fundamental sample unit level.General information about the source of the sample is also assigned atthe fundamental sample unit level.

In the example embodiment, the data is organized into a hierarchicalstructure of inter related data objects. For the example embodiment ageneric data organization has been developed that allows representationof the interrelationships between different samples, and allowssubdivision of the sample for analysis purposes. A “sample” in theexample embodiment is a physical sampling of material, for examplematerial taken from a processing plant, site or drill hole, or othersource for which analysis is required. The data organization is designedto store all of the essential information required for analysis, and toavoid duplication of data, minimizing storage and avoiding replicationissues.

General information relevant to e.g. the structure and measurement isseparable from the measurement data itself. The general information mayinclude details such as: where the original sample came from, how it wassampled, how the sample was prepared and how the samples relate to eachother. Some of the advantages provided by these separations within thedata organization are:

-   -   The general information can be changed without affecting the        measurement data.    -   The methods relevant to the data analysis and reporting can be        maintained and updated without affecting the general information        or the measurement data.

One of the issues is the mechanism by which one defines data structureand interrelationships. For example, each specific field of applicationwill have unique requirements for data organization and storage of thedata fields. These requirements will depend on how the sample is sourcedand the sample material itself.

In the example embodiment the data organization allows the use of“plug-in data schemas” to establish the data organization. Theseparation of the general information from the measurement datafacilitates the use of such plug-in schemas. This separation relies onencapsulating and “black boxing” the details of the data organization,so that the details are hidden from other parts of the analysis system.The “black box” data organization is implemented as an independentmodule. In the example embodiment such modules are referred to as a“schema”. To the rest of the analysis system, all schemas present auniform external interface. Internally their structure can beimplemented in any manner, so long as the external interface can conformto the standard.

In the example embodiment the hierarchical nature of the dataorganization is called the Sample Schema. Sample Schemas are availablefor a variety of application types. Sample schemas vary depending on thetype of material being measured and the type of analysis required. Forexample, a sample schema for forensic type analysis of data may havedifferent relationship terms in the hierarchy and/or a different numberof levels. Schemas could equally be implemented for geological,petrochemical or any other application of the technology. The presentembodiment shows an implementation of a schema specialized formetallurgical analysis. The analysis methods can be configured toinclude any or all of these schemas, without need for alterations to themethods outside of the schema.

The data analysis methods are set up to allow plug and play of as manydifferent sample schemas as required, for example: plant processing,mining exploration, coal, flyash, along with the metallurgical,forensics, etc. mentioned above. The processing relies on the existenceof a data structure, not on any particular type of sample schema. Thisis driven by the need to have an analysis tool across the full range ofuse of technology applications. The ability to separate the measurementdata (or particles) from the Sample Schema data allows for data analysisand re-analysis by any predetermined or customized Sample Schema.

In the example embodiment, the plug-in schema is for metallurgical typesamples. This will now be described. At the top of the hierarchicalstructure of inter-related data objects are the “Company” objects, eachrepresenting a business entity. Each company object contains one or more“Operation” objects representing sites, plants or divisions within thecompany. Each operation in turn can contain “Survey” objectsrepresenting a particular chronological point when a set of samples weretaken. Each survey then contains “Sample” objects, which correspond tothe physical samples of material taken during the survey. Each samplethen contains a number of “Fraction” objects, corresponding to physicalsubdivision of the sample material based on its particle size, whichoccurs during preparation for measurement. Each fraction contains“Sample Block” objects, corresponding to a physical block of materialprepared for placement in the SEM. Each sample block then has“Measurement” objects corresponding to an actual measurement of thatblock in a SEM. Each measurement object contains “Particle” objects thatcorrespond to the particle entities, i.e. clusters of measurement spotsthat were identified as constituting a single distinct particle on thesample block in the example embodiment. Amongst other things, eachparticle object contains the data for the spectral category of eachmeasurement spot it includes.

In the example embodiment, the hierarchy is expressed as a structure ofdata objects stored in an object-oriented database referred to as a“Datastore”. The datastore also contains data objects for the sets ofspectral category information and objects that embody the particleprocessing stream.

As the person skilled in the art will appreciate, there are countlessvarieties of spectral categories. These categories are based on thevarious spectral patterns arising from the nature of the physicalcomposition of the material such as element or compound or combinationthereof and may even include more subtle attributes of the samplematerial such as textural properties. The sample material wouldtypically contain unknown blends of material. Furthermore it is expectedthe volume of sample material excited by the electron beam in any singlespot may contain a plurality of materials. The outcome is an extensiverange of available spectral categories which in most cases is far toodetailed to use as the basis for analyses.

A “species identification profile” or SIP can be used to group themeasured X-Ray spectra into a first level of the predetermined spectralcategories. In the example embodiment, the SIP is an extensive librarywhich maps measurement spectra patterns to spectral categories. For easeof interpretation and data analysis, the SIP may still provide too muchdetail, and it may be more desirable to consider the measured data interms of the materials or other properties rather than compositions. Inorder to reduce the data to the required level of detail, a multi-stagehierarchical grouping in the spectral categories is used in the exampleembodiment.

The first stage is the SIP mentioned above. The second stage is the“Primary List”. The Primary List combines spectral categories from theSIP into smaller numbers of groups that are intended to correspond torecognizable materials and particularly common blends of materials.Since each Primary List material can represent a real material withknown physical properties—density, chemical composition, etc.—thoseproperties can be associated with the particle entries through theassignment of each particle entry to one of the Primary List stagecategories.

For many analyses, even the Primary List is too detailed, and analysismethods may require further simplification of the material listing downto a few application specific groupings. For this, a third level ofgrouping is provided referred to as the “Secondary List”. The SecondaryList groups Primary List categories into a smaller number of Secondarycategories. Data can then be analyzed and displayed at this secondarylevel.

By providing the above-described hierarchical grouping in the exampleembodiment, the complex detail of the original X-Ray spectra can bereduced to a simple, comprehensible, analysis. This grouping methodprovides flexibility, allowing grouping to be restructured at any level,without necessarily requiring changes in the hierarchical groupings atany of the other levels.

In order to analyze samples, a series of processing steps must beapplied to a selected subset of the particles stored in the database.The example embodiment implements what will be referred to as the“particle stream” model for processing and analyzing the data obtainedfrom the SEM measurements.

The “particle stream” model for processing and analyzing particle datawas developed to allow:

-   -   “Real time” calculation and display of processing and analysis        results.    -   Quick and easy alteration of the processing steps.    -   The application of two or more different sets of processing        steps to the same population, or to different populations, and        comparison of the results from each.

The “particle stream” model considers the data as a stream of individualparticles. The particles are drawn from a source and passed through asequential series of “stages”, each of which takes in one or morestreams of particles, combines them, then processes and subdivides thecombined particles into one or more output streams that can be passed tosubsequent processing stages.

Each output stream of a stage can be the input for more than onesubsequent stage, resulting in the ability to effectively duplicate aparticle stream and apply two different sets of processing to the sameparticles simultaneously. Each stage can accept input from more than onestream. This allows a stream to be split into disjoint streams at onestage, have different processing stages applied to each stream, and thenrecombine the split streams into one final stream for analysis. Thefinal stage of a processing stream will gather all of the particles fromits input streams and perform a statistical analysis of this particlepopulation and present that to the user in the form of a graph, table orvisual image of the particle population.

Each stage in the processing stream is a modular unit, and each unitpresents a standard interface to the rest of the data analysis system.Because of this, the stages can be combined in any order and in anysequence, and stages can be added or removed from any point in asequence of stages. This model allows significant flexibility in thecreation and manipulation of processing streams, enabling therequirements described above to be satisfied. The staged structure alsoprovides significant scope for optimizing performance; for example theresults can be stored in fast access memory at any stage.

By treating the data as a stream of particle entities, this model allowsfor powerful, flexible and extensible processing and statisticalanalysis.

In the example embodiment, Job data objects (described in more detailbelow with reference to FIG. 3) implement the particle stream processingmodel. When a Job is activated, it has a single particle stream whichdraws selected particles from the Datastore, through a customizableseries of pre-processor objects, into a staging population in the Jobobject. This staging population is the “statistically-representativepopulation” that is used in normalization calculations.

The Job object contains multiple Report objects, each of which embodiesa single input particle stream, drawing from the Job's stagingpopulation and terminating with an analysis and reporting stage. Theuser utilizing the software can choose to activate any combination ofthe Reports defined in the Job. When activated, the Report will draw theparticles from the Job staging population, through a customizable seriesof pre-processor objects, into the final Report object. There theparticles are accumulated into a report population, and analysis isperformed.

FIG. 3 outlines the process of selecting measurements from a Datastore300 to place into a Job in an example embodiment. The Datastore 300holds all of the measurements imported, for all of the differentSurveys, Operations and Companies a user has defined. The Job belongs toa particular Operation within the Datastore 300, and this automaticallyrestricts it to only accessing measurements that belong to that sameOperation.

When the Job is opened, it loads in a collection of particle entitiesfrom measurements in the Datastore 300. These particle entities form thestatistical basis for the subsequent analyses that are performed. Theparticle entities that are included in the Job are assumed to be astatistically representative sampling of a fraction of a particularproduct stream at the time of a survey. Thus if in the Job a particularmaterial makes up 50% of the particle entities in a fraction, then it isextrapolated that 50% of that fraction of the product stream consistedof that material.

The selection of a statistically-representative particle entitypopulation is done in two parts in the example embodiment:

-   -   Select a set of measurements to be included in the Job indicated        at numeral 304.    -   Pre-processors e.g. 306 are used to filter, edit and modify the        particle entities from the selected measurements.

The particle entities that result from this process of selection,filtering and processing then form the Job Statistical Base Populationat numeral 308, which is supplied to Reports that are opened in thatJob.

Processing and analysis typically consider the different spectralcategories found in the particles, and their relative abundances,dispositions and properties. As mentioned above, for ease ofinterpretation and analysis it is usually desirable to work at a lessspecific level of categorization than the spectral categories. In thisfashion additional physical (or other) properties are introduced todefine these less specific categories. This hierarchical grouping isapplied to the spectral categories in order to provide powerful andflexible data analysis. The example embodiment utilizes a three-levelhierarchy starting with the SIP (Species Identification Profile), then aPrimary and Secondary List. In the case of analyzing metallurgical datathe hierarchy can be defined as “Primary Mineral List”, which combinesdifferent spectral categories into a smaller number of groups intendedto correspond to known mineral classifications followed by the thirdlevel of the hierarchy, the “Secondary Mineral List”, which allowsprimary mineral list groups to be combined into an even smaller numberof groups for easier comprehension of the analysis results.

The Primary Mineral List allows additional physical properties, such asdensity and hardness, to be associated with each group. These propertiesare important to the calculations performed in the analysis of the data,as they link the measured data to the known physical characteristics ofactual minerals. The Secondary Minerals List provides for furthercustomization of the SIP groupings.

A Job also determines the Primary Mineral List to be used when analyzingmeasurements, and this in turn determines the SIP that must have beenused when originally categorizing the spectra taken in the measurements.In the example embodiment one can only analyze measurements that use thesame SIP as the Job. Primary List, and hence the SIP, are selected usingthe Job Properties. In the Job Properties one can select from any of thePrimary Mineral Lists available in the Datastore 300. Selecting aPrimary Mineral List automatically selects its corresponding SIP.

SIPs and Primary Lists are global in a datastore e.g. 300. SecondaryMineral Lists are specific to a Job. Secondary Lists can be importedinto a Job, and/or new Secondary Lists can be created for a Job.

The particle entities that are selected into a Job are very important,not just because these are the measurements that will be available toReports, but they are used as the statistical basis for many of thecalculations. Details of the role of the Statistical Base Population 308will be described below.

One can specify the total mass-flow that was passing through the productstream sampled. The measurements that are selected into a job areassumed to be a statistically representative sample of this flow in theexample embodiment. Therefore, if a particular mineral represents 50% ofthe selected particle entities, it is assumed from this that 50% of thesampled product stream consisted of that mineral, as mentioned above.When one then asks a question such as “what is the mass of element X inthis population”, the answer is based on how much element X is in thatpopulation, compared to how much is in the Statistical Base Population,adjusted by the mass that was flowing through the product stream at thetime of sampling.

Thus selection of an appropriate Statistical Base Population into a Jobis important in determining the reasonableness of the resultingcalculations. For calculations, the Statistical Base Population isdivided up by fraction and by measurement type, so one only needs a goodstatistical sample for the particular sample fractions and measurementtypes to be analyzed in a particular Report.

The Statistical Base Population 308 can take up a large amount ofcomputational resources. One of the advantages of the present analysissystem is that it enables the selection of only the requiredmeasurements needed at the time. When measurements are de-selected, theparticle entities and all their calculated properties are released fromresources such as computer memory.

A Report in the example embodiment is a plug-in analysis module. It canperform a particular analysis on the sample measurements provided to it.Some Reports are specialized for performing just one very particularanalysis. Other Reports are very generalized, and can be tailored toperform a wide variety of functions.

Some typical Reports in the example embodiment include:

3D Chart A general-purpose, customizable, 3D chart. Modal AnalysisPerforms a specialized modal-analysis of sample measurements. ParticleView Allows visual examination of the actual measurement data.

Reports are created within a Job e.g. 302. Each Report storesconfiguration properties that are set to control how the report appears,and how it analyzes the data you give it.

The Reports act on the sample measurements selected into the Job. Asthose selections are changed, the Report output is updated in theexample embodiment. When the analysis data required is obtained, theresults can be copied from a Report into another application, such asExcel® or Word®. Both chart images and the tabular data they representcan be copied.

FIG. 4 illustrates how data flows into a Report. Reports each belong toa Job, and a Job must be open in order to access the Report. Asdescribed above, the act of opening a Job and selecting somemeasurements creates a Job Particle Population 400 in the Job. EachReport opened draws in the Job Particle Population and analyzes it.

Selection Filtering 402 and preprocessors 404 can be applied to theReport to control what particle entities are analyzed. However,regardless of how one restricts which particle entities are analyzed bya Report, the report still refers back to the Job Particle Population400 and the general information (such as the sample properties) in theDatastore (not shown) in order to normalize the results for the totalmass, volume and surface flow figures.

As illustrated in FIG. 4 one can add filters 400 and preprocessors 402to a Report, e.g. 406. These can be used to control what particleentities are passed to the Report for analysis, and can apply editing orimage processing to the particle entities that are passed through. Forexample, one can eliminate the barren particle entities (that is, thoseparticles that contain none of a particular material) from a report byadding a Filter preprocessor with an appropriate expression.

As shown in FIG. 5, to add a preprocessor, the “Preprocessors” panel 500must be displayed in the report in the Preprocessors panel 502. The “+”button is clicked and the type of processor selected from the processorlist 504. Once a new processor e.g. 506 is added it can be edited byclicking, on its entry in the preprocessor list 504. In the panel 508,controls e.g. 512 will appear that allow to adjust the settings of thepreprocessor. For example, for a filter, a categorizer slot 509 willappear in the properties area 512. A categorizer can then be entered inthe slot 509 that will exclude the appropriate particle entities. Forexample, to exclude barren particle entities, an expression that testsif the area of a particular material is greater than zero is used. Ifthis expression is ‘true’, the particle entity will pass through thefilter. If the expression is ‘false’, the particle entity will berejected. A click on the “tick” 512 on the categorizer slot 509 invertsthe filter logic.

A list of Report Templates is displayed whenever one elects to add a newreport to a job in the example embodiment. Each report type has specificcapabilities. Typical standard templates in the example embodiment are:

General-purpose 2D chart General-purpose 3D chart General-purpose XYchart Particle Grid Particle View Liberation Mineral Association ModalAnalysis Operational Statistics Ore Characterization Recovery Analysis

The Report Templates are “plug-in” components in the example embodiment.This means that they are separate pieces of software to the mainapplication, and can be added or upgraded separately to the mainapplication. This modular system enables to add special custom-writtenreports to suit particular requirements.

FIG. 6 shows an example report 600, entitled “Particle View Report”, toexamine the individual particle entities e.g. 602 in samplemeasurements. The Particle View Report 600 allows to view an image ofthe particle entities e.g. 602 in sample measurements. It also displaysmaterial properties, such as mineral and element properties, at numerals604, 605 respectively, and sample properties (at numeral 606). TheParticle View area 608 can be used to mark individual particle entitiesas “bad”, so that they are ignored by all calculations. The controls 610in the Particle View Report 600 allow to select particle entities withinthe view area 608, to zoom in and out, and to sort the particle entitiese.g. 602 in the display.

In “select” mode, clicking on a particle entity e.g. 602 will selectjust that particle. Hold down the “Ctrl” key while clicking to toggleparticle entities e.g. 602 into or out of the selection. Click and dragto select particle entities e.g. 602, 612 within a box. Materialproperties such as the Mineral, Element and Sample properties 604, 605,606 displayed will be those of the selected particle entities. If thereare no selected particle entities, the properties will be those of thepopulation as a whole.

Report “Drill-Down” is a special capability of certain reports in theexample embodiment. It enables to “drill-down” or investigate furtherdetails about subsections of a report viewed. As a general rule, in areport that provides drill-down one can select some part of thedisplayed data (e.g. a particular column in a chart, or a cell in aParticle Grid), and pop up a new report dealing only with the subset ofparticle entities in the chosen subsection of the original report.

For example, in a Particle Grid Report 700 shown in FIG. 7, one canright-click on any cell e.g. 702 in the report 700 and pick “View as . .. ” to drill-down. This allows to pop up a new report (not shown) thatdisplays just the particle entities that were in the selected cell ofthe Grid area 704. If a change is made in the original Report 700—e.g.change the selected samples, or click a different cell in the Grid area704—the change will be reflected in the drill-down report.

The “drill-down” capability is made possible by the modular nature ofthe “particle stream” processing model underlying the implementation,and demonstrates some of the power and flexibility that model allows.Any sub-population of particles within a report can be made available asan output particle stream to further stages of processing, analysis anddisplay.

In “drill-down” one can either select a new report (of any type) todisplay, or select from a pre-configured Drill-Down Template report. ADrill-Down Template is any report whose Usage property has been set to“Drill-Down Template”. The reports that pop up for selection in adrill-down are transient—they are not saved in the Job, and willdisappear when the original report from which they were popped up isclosed. However, one can save a template of the pop-up report. This willenable an identically-configured report to pop up next time.

The Particle Grid Report 700 shown in FIG. 7 is a customizable reportthat uses two Categorizers 706, 708 and two Calculated Values e.g. 709,710 to divide the measurements into a grid 704 of particle entitypopulations, e.g. in cell 702. It then displays a thumbnail image of theparticle entities e.g. 712 in each grid cell e.g. 702. The twoCategorizers 706, 708 determine how the particle entities are divided inthe x and y axes. The two Calculated Values 709, 710 determine how thecategories on each axis are sorted.

The expressions used in the calculated Values 709, 710 Categorizers 706,708 allow for user definable constraints to be set at a more fundamentallevel than the preprocessors function described above. The expressionsystem in the example embodiment has access to data at all levels ofschema structure for all particle entities. Examples of user definablecalculations based on the properties of the particle entities fall intocategories such as:

-   -   Calculated properties such as area, mass and other properties        related to composition or texture.    -   Sample schema properties such as how the mass was flowing        through a particular location when the sample was taken.

The power and flexibility of the example embodiment is, to a large part,achieved through the use of customizable categorizations, calculationsand filtering. The foundation of all of these functionalities is amathematical expression language in the example embodiment. Thislanguage allows the writing of mathematical expressions that performcalculations based on the properties of minerals, measured particleentities, and collections of measured particle entities. Thisfunctionality is facilitated by recognizing the particle entity as thefundamental sample unit.

Expressions are the building-blocks for reports and are found primarilyin Particle Categorizers and Calculated Values (706, 708, 709, 710 seee.g. in FIG. 7). They define the categories displayed in charts andtables and the values calculated in those charts and tables. They arealso used to define sorting and to create filters to select subsets ofparticles from the particle stream, as described above with reference toFIGS. 3 and 4.

An expression is a sequence of operators and operands, and is applied toa particular context. When expressions are evaluated, it is done in aspecific ‘context’. The context is simply where and how the expressionis being used. For example, an expression being used to sort categorieson the axis of a chart is one context. A expression being used to filterthe particle entities going into a report is another context. Thecontext determines:

-   -   The mineral lists available to the expression.    -   The ‘target’ of the expression—whether it is calculated for each        particle entity individually or a collection of particle        entities as a whole (a ‘population’).    -   The appropriate return value of the expression—whether the        expression should calculate a number, a string or a Boolean        value.        Available Mineral Lists

All expressions are evaluated within the over-all context of thecurrently open Job, and the mineral lists available are those availablewithin the job. Thus there will always be one SIP mineral list and onePrimary mineral list. There may also be one or more Secondary MineralLists.

Expression Targets

The target of an expression is determined by where it is used.Expressions used in Particle Categorizers are always calculated forindividual particle entities, so their target is a particle. Expressionsused in Calculated Values are always calculated for a population, sotheir target is the population.

Understanding the expression's target is important, because a propertyin an expression refers to a property of the target. Thus if anexpression refers to “Area”, then when used in a Particle Categorizer itrefers to the Area of an individual particle. When used in a CalculatedValue, it instead refers to the sum-total area of all the particleentities in the population. This ability to determine properties foreither a single particle, or the equivalent property for an entirecollection of particles as a whole, is important to the implementationin the example embodiment. Each property that can be calculatedincludes, if possible, logic to support both cases. In cases where thecalculation cannot be performed, an error message is produced.

In the example embodiment, all available properties can be calculatedfor both the particle and collection cases, except for the “ShapeFactor” property, which can only be calculated for an individualparticle. One simple case might be the calculation of what proportion ofthe cross-sectional area of a collection of particles occurs in the formof the mineral Pyrite. To do this, the software will first determinewhich spectral categories are to be considered “Pyrite”. Assuming“Pyrite” is a secondary mineral list grouping, the software willdetermine what primary mineral list groupings are included in “Pyrite”,and then what SIP spectral categories are included in those primarymineral list groupings. This will result in a (potentially large) listof spectral categories that are considered to be “Pyrite”. The softwarewill then iterate through all of the particles in the collection ofparticles, and for each particle count the total number of measurementspots that were assigned a spectral category that is considered to be“Pyrite”. This gives a measure of the cross-sectional area of “Pyrite”in the particle. In the case of area, the total area of “Pyrite” for acollection of particles can be obtained by simply summing the areas forthe individual particles.

A more complex example is the calculation of mass—for example, the massof “Pyrite” represented by a particle collection. This calculationproceeds in the same manner as the “Area” example above, but the areatotals for each particle have to be summed at the Primary Mineral Listgrouping level. At this point, the area totals for each primary minerallist grouping are multiplied by the Density property assigned to thatprimary mineral list grouping, to give a dimensionless “mass unit”value. The mass unit values can then be summed to give a total mass unitvalue for “Pyrite” in that particle.

The total mass is calculated by summing the Pyrite mass for eachparticle, but to do that, each particle's pyrite mass units value has tobe converted into a single uniform frame of reference—that of absolutemass. This process is referred to as “normalization”, and requires thestatistically-representative population mentioned earlier.

To normalize the mass units, it is necessary to first calculate thetotal corresponding mass units present in the statisticallyrepresentative population. Having done this, one only needs to know whatabsolute mass is represented by the statistically representativepopulation. This is done by subdividing the statistically representativepopulation on the basis of the sample and fraction objects of thehierarchical data organization in the example embodiment, as describedabove. For each sample object, the general information includes thetotal absolute mass of which that sample object is a statisticalrepresentative. For each fraction object, the general informationincludes the proportion of the sample object mass that occurs in thatfraction object.

Because each particle retains all of its contextual information throughthe inter-relation, inter alia, between particle objects, sample objectsand fraction objects, it is known which sample and fraction object eachparticle belongs to, and so the total absolute mass, and the total massunits, for the statistically representative population can bedetermined. The mass units for each particle can be converted toabsolute mass, and accumulated with the absolute masses of otherparticles. It is noted that this is regardless of the sample or fractionfrom which the respective particles originated. This enables“normalized” analysis for combinations and comparisons of particles fromdifferent parts of a physical sample and/or from different physicalsamples.

In the course of analyzing particles, some situations require the methodto process particles that are touching or almost touching. When samplesare measured, it is important that each physical particle be recognizedas such. Because of the physical limitations of the sample preparationprocess, it is not possible to guarantee that all particles in thesample are physically separated. The situation may arise where particleswill be in contact, or appear to be so at the resolution of the measureddata. In the case where the proportion of “touching particles” issignificant then treating a group of two or more closely spacedparticles as a single particle may lead to incorrect data analysis andinterpretation.

The example embodiment includes a method for detecting when a particlemeasurement may actually be several separate particles, and a method forsplitting the data for such a particle into separate “particles” forsubsequent analysis. Existing algorithms for this type of processtypically use image analysis of “grey scale” images, and usepixel-oriented processing algorithms such as “erode”, “dilate” and“watershed algorithms”. The example embodiment uses a differentapproach, based on the measured material information and the perimeterprofile of the particle. The method examines the perimeter profile ofthe particle, looking for “cusps” and indentations as clues to touchingparticles.

The method then uses the measured material data to calculate possible“split paths” representing where the touching particles touch. In doingthis, it uses heuristic logic to calculate a path that preferentiallyfollows boundaries between materials. The method relies on the knowledgeof materials and compositions (as indicated by the spectral categories)that are likely to be detected at the boundary between two touchingparticles.

In order to determine if a particular particle is in fact two or moretouching particles, a recursive analysis is performed in the exampleembodiment as follows:

-   -   Firstly the bounding dimensions of the particle are considered        based on the measurement spots assigned to the particle, and        particles that are deemed too small in either dimension are        rejected as “non-touching”.    -   Next an approximate measure of the “roundness” of the particle        is determined, by considering the ratio of the square of its        perimeter to its area. Particles that fall below a certain limit        are rejected as “non-touching”.    -   The perimeter profile of the particle is then analyzed, to        detect “cusps”—places where the perimeter dips towards the        opposite side. This is done using a two-stage process: first        selecting a set of arbitrary “probe points” around the perimeter        and looking for cusps that dip towards those points, and then        repeating that process using the identified cusp points as the        “probe points”.    -   The cusp-detection process is then repeated a third time, using        the cusp points obtained from the preceding stage. This produces        a list of “mutually interested” cusp pairs—pairs of cusp that        appear to dip towards each other.    -   This list of cusp pairs is then culled, based on some simple        geometric analysis of the potential splits that they would        produce. Splits that are considered “too long” or fragments that        would be “too large” or “too small” according to a set of        adjustable parameters are rejected.    -   Any cusp pairs that survive the simple geometric cull are then        analyzed for “relative velocity”. This is a heuristic analysis        of the overall shape of the cusp, assessing how steep and sharp        it is. This is done by treating the “target cusp” as a        gravitational attractor, and simulating the motion of a body        moving around the perimeter to the cusp being analyzed. The        simulated velocity of the body when it reaches the cusp is the        “relative velocity” of the cusp. The relative velocity is        assessed both clockwise and counter-clockwise. Certain damping        and attenuation factors are applied in the simulation to        emphasize the localized effects around the cusp itself.    -   If the total relative velocity of a cusp pair is insufficient,        it is culled from the list.    -   If there are any cusp pairs remaining, the particle is        considered to in fact be two or more touching particles.    -   If we then wish to split the particle, we perform a detailed        split-analysis. This consists of calculating an optimal-path        split through the particle for each remaining cusp pair. The        “cost” of the path is assessed based on the spectral categories        or groupings of the points the split-path passes between. The        path with the lowest “cost” is then selected, and the particle        split on that basis.    -   The same analysis process is then re-applied to each particle        resulting from the split.

This analysis determines whether the fragment should be represented aspart of the particle or a separate particle.

When a QEMSCAN system measures a sample, the resulting data is a map ofcompositions, which is interpreted to determine the materials present inthe sample. Because the electron beam used to scan the sample excites avolume, and the volume usually contains more than one material, thecompositions measured represent varying blends of materials present.This process often produces undesirable artefacts at the boundariesbetween different materials.

The example embodiment includes a method to eliminate such boundaryartefacts. This enables accurate analysis of the data. The method uses arules-based pattern recognition system to first identify then eliminatethe boundary-phase artefacts. The rules utilized by the system areexpressed in terms of material categories at one of the levels of themulti-level hierarchical grouping of measured data. In the exampleembodiment, the rules are applied at the Primary List level.

The rules-based system uses a three-point filter, which is appliedacross the rows and/or down the columns of the spectral category datawithin each fundamental sample unit. The filter examines each cluster ofthree data points in turn, and applies its pattern-recognition rules.The rules define allowed transformations of the “middle” data point ineach cluster of three, based on its spectral category and the spectralcategory of the two adjacent data points. In general, if the pattern ofspectral categories matches one of the rules, the middle data point willbe changed to be the same category as either the preceding or thefollowing data point. The rules can be defined by the software operator,based on their knowledge of the materials being analyzed and theartefacts typically encountered in their measurements.

The method and system of the example embodiment can be implemented on acomputer system 800, schematically shown in FIG. 8. It may beimplemented as software, such as a computer program being executedwithin the computer system 800, and instructing the computer system 800to conduct the method of the example embodiment.

The computer system 800 comprises a computer module 802, input modulessuch as a keyboard 804 and mouse 806 and a plurality of output devicessuch as a display 808, and printer 810.

The computer module 802 is connected to a computer network 812 via asuitable transceiver device 814, to enable access to e.g. the Internetor other network systems such as Local Area Network (LAN) or Wide AreaNetwork (WAN).

The computer module 802 in the example includes a processor 818, aRandom Access Memory (RAM) 820 and a Read Only Memory (ROM) 822. Thecomputer module 802 also includes a number of Input/Output (I/O)interfaces, for example I/O interface 824 to the display 808, and I/Ointerface 826 to the keyboard 804. The components of the computer module802 typically communicate via an interconnected bus 828 and in a mannerknown to the person skilled in the relevant art.

The application program is typically supplied to the user of thecomputer system 800 encoded on a data storage medium such as a CD-ROM orfloppy disk and read utilizing a corresponding data storage medium driveof a data storage device 830. The application program is read andcontrolled in its execution by the processor 818. Intermediate storageof program data may be accomplished using RAM 820.

It will be appreciated by a person skilled in the art that numerousvariations and/or modifications may be made to the present invention asshown in the specific embodiments without departing from the spirit orscope of the invention as broadly described. The present embodimentsare, therefore, to be considered in all respects to be illustrative andnot restrictive.

We claim as follows:
 1. A method of analysing spectroscopic data the method comprising: directing an electron beam onto a sample; collecting spatially resolved measurement x-ray spectroscopic data of a sample for a series of measurements spots, the x-rays generated by the impact of the electron beam at each measurement spot; assigning the measurement spots into a predefined set of spectral categories, based on characteristics of the spectroscopic data of the respective measurement spots; identifying groupings of the measurement spots based on their respective spectral categories and their spatial relationships; assigning each grouping of measurement spots to a fundamental sample unit data object; and utilizing a hierarchical structure of general information data objects that embody the hierarchical relationships of the general information assigned to the fundamental sample unit data objects, with relationships defined as being either “up” the hierarchy, that is away from the fundamental sample unit data objects, or “down” the hierarchy, that is towards the fundamental sample unit data object, and wherein the general information assigned to a fundamental sample unit data object is stored in the general information data object in the hierarchical structure that represents the manner in which said general information data is shared by the fundamental sample unit data objects.
 2. The method as claimed in claim 1, wherein data items obtainable from each general information data object in the hierarchical structure comprise all of the data items stored in said general information data object, plus all data items obtainable from general information data objects further “up” the hierarchical structure.
 3. The method as claimed in claim 1, wherein the hierarchical structure and choice of storage locations within the hierarchical structure follow a predefined pattern.
 4. The method as claimed in claim 1, wherein the hierarchical structure and choice of storage locations within the hierarchical structure are determined and changed dynamically as-needed.
 5. The method as claimed in claim 1, further comprising assigning one or more properties to each spectral category.
 6. The method as claimed in claim 1, further comprising assigning general information, including measurement information and/or sample information, to each fundamental sample unit data object.
 7. The method as claimed in claim 6, further comprising calculating one or more derived properties for each fundamental sample unit data object based on one or more of a group comprising the measurement spots assigned to the fundamental sample unit data object, the one or more properties assigned to the spectral categories of the measurement spots, the general information assigned to the fundamental sample unit data object, and the spatial relationships of the measurement spots.
 8. The method as claimed in claim 7, wherein the derived properties comprise one or more of a group comprising mass, area, perimeter, volume, size and density.
 9. The method as claimed in claim 1, wherein the predefined set of spectral categories comprises a hierarchical grouping of categories.
 10. A method of analysing spectroscopic data the method comprising: directing an electron beam onto a sample; collecting spatially resolved measurement x-ray spectroscopic data of a sample for a series of measurements spots, the x-rays generated by the impact of the electron beam at each measurement spot; assigning the measurement spots into a predefined set of spectral categories, based on characteristics of the spectroscopic data of the respective measurement spots; identifying groupings of the measurement spots based on their respective spectral categories and their spatial relationships; assigning each grouping of measurement spots to a fundamental sample unit data object; formulating an analysis query; and defining the analysis query as a sequential series of processing stages, each processing stage having one or more inputs and one or more outputs; wherein, during execution of the analysis query, one or more of the fundamental sample unit data objects are sequentially provided to each processing stage input as input streams, and processed and output as respective output streams of fundamental sample unit data objects at each processing stage output; and wherein the output stream or streams from one processing stage are the input streams for the next processing stage in the sequential series of processing stages.
 11. The method as claimed in claim 10, wherein the processing at each processing stage comprises: passing a received fundamental sample unit data object as-is to one or more processing stage outputs, or creating one or more new fundamental sample unit data objects based on the received fundamental sample unit data object, wherein the new fundamental sample unit data objects inherit the general information assigned to the received fundamental sample unit data object and retain a reference back to the received fundamental sample unit data object, and passing each new fundamental sample unit data objects to one or more processing stage outputs.
 12. The method as claimed in claim 11, wherein one or more logical expressions are utilised for assigning the received or the new fundamental sample unit data objects to one or more of the processing stage outputs.
 13. The method as claimed in claim 11, wherein the new fundamental sample unit data object is created to separate respective groupings of measurement spots which were initially assigned to one fundamental sample unit data object.
 14. The method as claimed in claim 11, wherein one of the processing stages produces a statistically representative population of fundamental sample unit data objects as the output stream for normalisation processing in subsequent processing stages.
 15. The method as claimed in claim 14, wherein the statistically representative population of fundamental sample unit data objects comprises fundamental sample unit data objects from different samples.
 16. The method as claimed in claim 10, further comprising assigning one or more properties to each spectral category.
 17. The method as claimed in claim 10, further comprising assigning general information, including measurement information and/or sample information, to each fundamental sample unit data object.
 18. The method as claimed in claim 10, wherein the predefined set of spectral categories comprises a hierarchical grouping of categories. 